Recursive Update Filtering for Nonlinear Estimation

Zanetti, Renato

doi:10.1109/tac.2011.2178334

Cited by 46 publications

(45 citation statements)

References 8 publications

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“…The EKF has just one step in the update procedure, and the IEKF is an iterative filtering method derived by the Netwon method. The RUF [7,8] is an another iterative filtering method derived by the LMMSE, not the MAP, and it has the fixed number of steps.…”

Section: Simulationmentioning

confidence: 99%

Bayesian Nonlinear Filtering via Information Geometric Optimization

Cheng

et al. 2017

Entropy

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Abstract:In this paper, Bayesian nonlinear filtering is considered from the viewpoint of information geometry and a novel filtering method is proposed based on information geometric optimization. Under the Bayesian filtering framework, we derive a relationship between the nonlinear characteristics of filtering and the metric tensor of the corresponding statistical manifold. Bayesian joint distributions are used to construct the statistical manifold. In this case, nonlinear filtering can be converted to an optimization problem on the statistical manifold and the adaptive natural gradient descent method is used to seek the optimal estimate. The proposed method provides a general filtering formulation and the Kalman filter, the Extended Kalman filter (EKF) and the Iterated Extended Kalman filter (IEKF) can be seen as special cases of this formulation. The performance of the proposed method is evaluated on a passive target tracking problem and the results demonstrate the superiority of the proposed method compared to various Kalman filter methods.

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Section: Simulationmentioning

confidence: 99%

Bayesian Nonlinear Filtering via Information Geometric Optimization

Cheng

et al. 2017

Entropy

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“…This prompts investigations into lightweight algorithms for approximate and accurate inference. Examples that improve the update step are the recursive update filter (RUF) [18] and the progressive Gaussian filters (PGF42/PGFL) [19], [20]. However, RUF only operates under the additive Gaussian noise assumption, PGF42 only operates under the Gaussian excitation assumption, and the PGFL requires a tractable likelihood, which is not always available.…”

Section: Introductionmentioning

confidence: 99%

Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments

Tronarp

García-Fernández

Särkkä

2018

IEEE Signal Process. Lett.

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Iterative filtering and smoothing in non-linear and non-gaussian systems using conditional moments," IEEE Signal Processing Letters, vol. PP, no. 99, pp. 1-1, 2018 DOI:10.1109/LSP.2018.2794767 Copyright:Copyright 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Abstract-This letter presents the development of novel iterated filters and smoothers that only require specification of the conditional moments of the dynamic and measurement models. This leads to generalisations of the iterated extended Kalman filter, the iterated extended Kalman smoother, the iterated posterior linearisation filter, and the iterated posterior linearisation smoother. The connections to the previous algorithms are clarified and a convergence analysis is provided. Furthermore, the merits of the proposed algorithms are demonstrated in simulations of the stochastic Ricker map where they are shown to have similar or superior performance to competing algorithms.

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“…Generally, in the sense of minimum mean square error (MMSE), there is a lack of optimal estimator for nonlinear dynamic state estimation problem because the closed form solution of its posterior probability density function (PDF) is unavailable, and linear MMSE (LMMSE) estimators are usually obtained based on Gaussian approximation to such PDFs in most applications [2,9,22]. However, LMMSE estimator may fail in some applications with large prior uncertainty and high measurement accuracy [5,10,18], which motivates the research on iterated Kalman-type filter (IKTF) [13] and progressive Gaussian filtering (PGF) [7,8,11,14].…”

Section: Introductionmentioning

confidence: 99%

“…In [8] and [14], PGFs based on deterministic Dirac mixture approximation method are proposed, which have higher estimation accuracy than standard Gaussian-approximated filter. On the other hand, by means of the idea of progressive update, an EKF with recursive measurement update is proposed, which utilizes linear measurement update gradually, i.e., measurement information is extracted gradually [18]. However, as discussed in [18], similar to classical EKF algorithm, it suffers the following drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, by means of the idea of progressive update, an EKF with recursive measurement update is proposed, which utilizes linear measurement update gradually, i.e., measurement information is extracted gradually [18]. However, as discussed in [18], similar to classical EKF algorithm, it suffers the following drawbacks. (i) It needs to assume that Jacobian matrixes of nonlinear process and measurement functions are available.…”

Section: Introductionmentioning

confidence: 99%

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Design of Sigma-Point Kalman Filter with Recursive Updated Measurement

Huang

Zhang

et al. 2015

Circuits Syst Signal Process

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In this study, the authors focus on improving measurement update of existing nonlinear Kalman approximation filter and propose a new sigma-point Kalman filter with recursive measurement update. Statistical linearization technique based on sigma transformation is utilized in the proposed filter to linearize the nonlinear measurement function, and linear measurement update is applied gradually and repeatedly based on the statistically linearized measurement equation. The total measurement update of the proposed filter is nonlinear, and the proposed filter can extract state information from nonlinear measurement better than existing nonlinear filters. Simulation results show that the proposed method has higher estimation accuracy than existing methods.

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Recursive Update Filtering for Nonlinear Estimation

Cited by 46 publications

References 8 publications

Bayesian Nonlinear Filtering via Information Geometric Optimization

Bayesian Nonlinear Filtering via Information Geometric Optimization

Iterative Filtering and Smoothing in Nonlinear and Non-Gaussian Systems Using Conditional Moments

Design of Sigma-Point Kalman Filter with Recursive Updated Measurement

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